The DOI matches its Crossref record; 88 of the 89 references it cites were found in a registry or an index.
Artificial intelligence in COVID-19 drug repurposing
Open accessAbstract
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
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“In some cases, these AI-powered approaches have been able to identify promising drug candidates in a fraction of the time that it would take when using traditional methods [26,27,28,29,30,31].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [28]
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“ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating the virus.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [28]
In the citing paper
“The study is a strong proof for the use of AI-assisted tools for drugs and repayment for human diseases, including COVID-19 in times of fatal disease [11].”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [11]
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The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690
The Ethics of Artificial Intelligence
Floridi, L. (2023) · doi:10.1093/oso/9780198883098.001.0001 · Cited by 682
Artificial intelligence in drug development
Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G. L., Li, X., Wu, J. C., & Yang, S. (2025) · Nature Medicine · doi:10.1038/s41591-024-03434-4 · Cited by 585
“An advanced pipeline has been created that integrates multimodal datasets, generates molecular leads using AI, ranks them by efficacy and safety, and uses deep reinforcement learning to create patentable analogs for testing 212 .”
Building a knowledge graph to enable precision medicine
Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522
Artificial intelligence in cancer target identification and drug discovery
You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 499
“The key to identify therapeutic targets and discover drugs using ML-based biology analysis is to make use of network features in biological networks.”
BackgroundMethod
Metadata
- Authors
- Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F.
- Affiliations
- Cleveland Clinic (US)
- Cornell University (US)
- HEC Montréal (CA)
- Tel Aviv University (IL)
- Corresponding author
- Cheng, Feixiong
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 2
- Issue
- 12
- Pages
- e667-e676
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7500917
- OpenAlex
- W3087156149
- Cited by
- 636
- References
- 89
- Citations by year
- 2026: 50 · 2025: 74 · 2024: 73 · 2023: 131 · 2022: 117 · 2021: 173 · 2020: 18
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- reponame:Expeditio Repositorio Institucional UJTL
- Funders
- National Science Foundation (2027970, 1750326, HHSN261200800001E)
- Cleveland Clinic
- Frederick National Laboratory for Cancer Research (HHSN261200800001E.)
- National Institutes of Health (#HHSN261200800001E, COVID-19, HHSN261200800001C)
- National Institute on Aging (R01AG066707)
- National Heart, Lung, and Blood Institute (R00HL138272, HHSN261200800001E)
- Office of Naval Research (N00014-18-1-2585, N00014)
- National Cancer Institute (HHSN261200800001E))
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- drug repurposing, COVID-19 pandemic, artificial intelligence, network medicine, precision medicine, drug target identification, drug development acceleration, big data
- MeSH terms
- SARS-CoV-2, COVID-19 Drug Treatment, Algorithms, Antiviral Agents, Artificial Intelligence, Drug Therapy, Combination, Humans, Treatment Outcome, Drug Discovery, Drug Repositioning
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